Year: 2025
Type: article
Abstract: This article explores the evolution and integration of hybrid threat detection systems in modern cybersecurity architectures, combining traditional rule-based approaches with artificial intelligence methodologies. The article examines how these hybrid systems enhance detection capabilities while addressing the limitations of standalone solutions. Through a comprehensive analysis of both rule-based and AI-driven approaches, the article demonstrates the effectiveness of hybrid architectures in improving threat detection accuracy, reducing false positives, and enhancing response times to emerging threats. The article further investigates implementation challenges and presents solutions for organizations adopting hybrid security frameworks, emphasizing the importance of balanced integration strategies and ongoing system maintenance.
Author: Imran Ahmed Shaik
Institution: University of Illinois Chicago
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411539121